{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 把数据所代表的的图片显示出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images_and_labels = list(zip(digits.images, digits.target))\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline\n",
    "plt.figure(figsize=(8, 6), dpi=200)\n",
    "for index, (image, label) in enumerate(images_and_labels[:8]):\n",
    "    plt.subplot(2, 4, index + 1)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.title('Digit:%i' % label, fontsize=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(digits.data,digits.target,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,\n  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n  max_iter=-1, probability=False, random_state=None, shrinking=True,\n  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(gamma=0.001,C=100.)\n",
    "clf.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看预测情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uFm7tYbQYERERiIqKMtvvN10S/fFH4OJFw7KzM/DJJ0DHjnXbOTgYkmhyMnDw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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ypred = clf.predict(Xtest)\n",
    "fig, axes = plt.subplots(4, 4, figsize=(8, 8))\n",
    "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(Xtest[i].reshape(8, 8), cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    ax.text(0.05, 0.05,\n",
    "            str(Ypred[i]), fontsize=32, transform=ax.transAxes, color='green' if Ypred[i] == Ytest[i] else 'red')\n",
    "    ax.text(0.8, 0.05, str(Ytest[i]), fontsize=32, transform=ax.transAxes, color='black')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型保存与加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['digits_svm.pkl']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(clf, 'digits_svm.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = joblib.load('digits_svm.pkl')\n",
    "Ypred = clf.predict(Xtest)\n",
    "clf.score(Xtest, Ytest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
